Where to find facial artery perforators: a reference point

Reconstructive surgery of the midface using facial artery perforator (FAP) flaps is being used more frequently now as it has been reported to provide better aesthetic results and reduce a traditional two-stage procedure to a one-stage technique. Wide acceptance of this approach is limited by poor understanding of the anatomy associated with this technique however. This was investigated through a cadaveric study. The facial artery (FA) of 16 cadaveric half faces were each identified, cannulated with coloured latex, and then dissected to give an accurate and quantified description of FA perforating branches. A lateral view picture of each specimen was taken and analysed using ImageJ 1.42q. Cadaveric dissections showed that each hemiface could be regarded as a single entity. Means: FA length = 116±22 mm, FA diameter = 2.62±0.74mm, number of FAPs = 4±2, FAP length = 14.12±3.46 mm, FAP diameter = 0.94±0.29 mm. A reference point, A, where FAPs were consistently found to originate was also identified. Therefore, the FAP flap is a viable and valuable addition to plastic reconstructive techniques. The localisation of point A with precise measurements can facilitate the design and use of such FAP flaps for the reconstruction of nasal, as well as perinasal and perioral defects

Delayed Fast Neutron as an Indicator of Burn-Up for Nuclear Fuel Elements

Feasibility study of burn-up analysis and monitoring using delayed fast neutrons was investigated at Missouri University of Science and Technology Reactor (MSTR). Burnt and fresh fuel elements were used to collect delayed fast neutron data for different power levels. Total reactivity varied depending on the burn-up rate of fuel elements for each core configuration. The regulating rod worth was 2.07E-04 Δk/k/in and 1.95E-04 Δk/k/in for T121 and T122 core configurations at 11 inch, respectively. Delayed fast neutron spectrum of F1 (burnt) and F16 (fresh) fuel elements were analyzed further, and a strong correlation was observed between delayed fast neutron emission and burn-up. According to the analyzed peaks in burnt and fresh fuels, reactor power dependency was observed and it was determined that delayed neutron provided more reliable results at reactor powers of 50 kW and above

Calculation and Tabulation of Efficiencies for Tungsten Foil Positron Moderators

Monte Carlo radiation transport simulations were used to calculate the positron stopping profiles in tungsten positron moderator foils. Stopping profiles were numerically integrated with efficiency kernels derived from Green\u27s function solutions of the 3D diffusion equation to determine the moderation efficiency in both the backscattering and transmission geometries. Stopping profiles and efficiencies were calculated for positron energies from 10 keV to 10 MeV and incident angles from 0° to 75°. The resulting efficiencies agreed with other calculated and measured values in the literature, especially when similar values of the positron diffusion length and surface emission branching ratio were used. Large discrepancies with some of the values reported in the literature are mainly attributed to differences in foil manufacture and surface condition - factors which are known to greatly influence the diffusion length - as well as work function and branching ratios. This work provides tabulated efficiencies for tungsten foil moderators that can be interpolated and integrated with a positron flux having arbitrary energy and angular distributions

Towards a reliable seamless mobility support in heterogeneous IP networks

Next Generation networks (3G and beyond) are evolving towards all IP based systems with the aim to provide global coverage. For Mobility in IP based networks, Mobile IPv6 is considered as a standard by both industry and research community, but this mobility protocol has some reliability issues. There are a number of elements that can interrupt the communication between Mobile Node (MN) and Corresponding Node (CN), however the scope of this research is limited to the following issues only: • Reliability of Mobility Protocol • Home Agent Management • Handovers • Path failures between MN and CN First entity that can disrupt Mobile IPv6 based communication is the Mobility Anchor point itself, i.e. Home Agent. Reliability of Home Agent is addressed first because if this mobility agent is not reliable there would be no reliability of mobile communication. Next scenario where mobile communication can get disrupted is created by MN itself and it is due to its mobility. When a MN moves around, at some point it will be out of range of its active base station and at the same time it may enter the coverage area of another base station. In such a situation, the MN should perform a handover, which is a very slow process. This handover delay is reduced by introducing a “make before break” style handover in IP network. Another situation in which the Mobile IPv6 based communication can fail is when there is a path failure between MN and CN. This situation can be addressed by utilizing multiple interfaces of MN at the same time. One such protocol which can utilize multiple interfaces is SHIM6 but it was not designed to work on mobile node. It was designed for core networks but after some modification in the protocol , it can be deployed on mobile nodes. In this thesis, these issues related to reliability of IPv6 based mobile communication have been addressed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Molecular classification of breast cancer using IHC markers: experience from a tertiary cancer center in south India

Background: Breast cancer is a very heterogeneous disease. Molecular or intrinsic subtypes of breast cancer are based on the gene expression profiling. Doing gene expression profiling in each case is practically difficult. So most of the labs depend on immunohistochemistry to classify breast tumors into various molecular-like subtypes. In this study, we have used immune histochemistry to classify tumors into various subtypes. Methods: We have retrospectively collected the data of breast cancer patients treated at Apollo Cancer Center, Chennai, in whom ER, PR, HER 2 Neu and Ki 67 were done, and the data was analyzed. Results: The commonest molecular subtype observed in the present study was Luminal B HER2 positive, constituting 40% of the cases, followed by a HER2 positive (non-luminal) subtype in 20% of cases. The triple negative subtype was the third most frequent, comprising 18% of the cases. The least frequent subtype was Luminal A, seen in only 8% of cases. Conclusions: There is a higher proportion of luminal B HER2 positive and triple negative subtypes in our study population compared to the other studies in published literature. The proportion of luminal A was lesser in our study compared to the literature

Inter-observer variability in diagnosing radiological features of aneurysmal subarachnoid hemorrhage; a preliminary single centre study comparing observers from different specialties and levels of training

BACKGROUND: A noncontrast computed tomography (CT) scan remains the initial radiological investigation of choice for a patient with suspected aneurysmal subarachnoid hemorrhage (aSAH). This initial scan may be used to derive key information about the underlying aneurysm which may aid in further management. The interpretation, however, is subject to the skill and experience of the interpreting individual. The authors here evaluate the interpretation of such CT scans by different individuals at different levels of training, and in two different specialties (Radiology and Neurosurgery). METHODS: Initial nonontrast CT scan of 35 patients with aSAH was evaluated independently by four different observers. The observers selected for the study included two from Radiology and two from Neurosurgery at different levels of training; a resident currently in mid training and a resident who had recently graduated from training of each specialty. Measured variables included interpreter's suspicion of presence of subarachnoid blood, side of the subarachnoid hemorrhage, location of the aneurysm, the aneurysm's proximity to vessel bifurcation, number of aneurysm(s), contour of aneurysm(s), presence of intraventricular hemorrhage (IVH), intracerebral hemorrhage (ICH), infarction, hydrocephalus and midline shift. To determine the inter-observer variability (IOV), weighted kappa values were calculated. RESULTS: There was moderate agreement on most of the CT scan findings among all observers. Substantial agreement was found amongst all observers for hydrocephalus, IVH, and ICH. Lowest agreement rates were seen in the location of aneurysm being supra or infra tentorial. There were, however, some noteworthy exceptions. There was substantial to almost perfect agreement between the radiology graduate and radiology resident on most CT findings. The lowest agreement was found between the neurosurgery graduate and the radiology graduate. CONCLUSION: Our study suggests that although agreements were seen in the interpretation of some of the radiological features of aSAH, there is still considerable IOV in the interpretation of most features among physicians belonging to different levels of training and different specialties. Whether these might affect management or outcome is unclear

Efficient framework for brain tumor detection using different deep learning techniques

The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms. In this paper, transfer-learning-based models in addition to a Convolutional Neural Network (CNN) called BRAIN-TUMOR-net trained from scratch are introduced to classify brain magnetic resonance images into tumor or normal cases. A comparison between the pre-trained InceptionResNetv2, Inceptionv3, and ResNet50 models and the proposed BRAIN-TUMOR-net is introduced. The performance of the proposed model is tested on three publicly available Magnetic Resonance Imaging (MRI) datasets. The simulation results show that the BRAIN-TUMOR-net achieves the highest accuracy compared to other models. It achieves 100%, 97%, and 84.78% accuracy levels for three different MRI datasets. In addition, the k-fold cross-validation technique is used to allow robust classification. Moreover, three different unsupervised clustering techniques are utilized for segmentation